On-line advertising is an important technology that can generate revenues, which in turn, can make vast amounts of content and information freely available to the Internet users. On-line advertising often includes tracking user activities on the Internet and advertising based on the information collected via tracking. Tracking based advertising often raises security and user-privacy concerns, but can also increase the relevance of an advertisement to a user. As such, the Internet users may be distracted less by messages of little interest to them, and the advertisers can increase return on their investments, as the users are more likely to respond to the displayed messages that are relevant to them than random messages.
To facilitate on-line advertising, when an Internet user visits a website such as cnn.com, amazon.com, etc., a tracking technology is usually loaded on the user's computer. Tracking technologies generally include any element that can collect user data and/or implement certain functionalities, such as delivering an advertisement, installing and/or running a script, etc., based on the user data. Tracking technologies include cookie-based trackers, advertisers, publishers, ad networks, intelligence or analytics providers, ad exchanges, retargeters, conversion pixels, beacons, and widgets. A tracking ecosystem is generally considered the set of elements or technologies and their interrelationships across the Internet. An impression is an instance of any one of these tracking technologies loading on a browser, whether that load results in anything visible to the user or not.
In order to accurately measure on-line browsing behavior of a user and/or to determine the effectiveness of a tracking technology (e.g., in terms of data collection, display of messages, etc.), it is beneficial to estimate one or more characteristics of the impressions. These characteristic include impression volume across the Internet corresponding to a certain tracking technology, impression volume associated with a host of interest such as cnn.com, amazon.com. etc., the estimated number of unique viewers/recipients of a particular technology and/or webpage, etc. Some methods and systems for generating impression-related estimates merely collect data associated with the browsing activities of randomly selected users. Many of these systems do not account for the specific nature of the tracking technology to be analyzed, and do not distinguish between human browsing activity and machine-generated browsing activity. The machine-generated activity can be of little relevance to the impression characteristics, because any technology loaded due to such activity is unrelated to any human behavior or viewing. An improved method and system is therefore needed to facilitate estimation of impression data.